ColossalAI/tests/test_comm/test_object_list_p2p.py

107 lines
3.6 KiB
Python

import pytest
import torch
from colossalai.communication.p2p import (
recv_backward,
recv_forward,
send_backward,
send_backward_recv_forward,
send_forward,
send_forward_recv_backward,
)
from colossalai.context import ParallelMode
from colossalai.core import global_context as gpc
from colossalai.initialize import launch
from colossalai.testing import rerun_if_address_is_in_use, spawn
CONFIG = dict(parallel=dict(pipeline=2))
torch.manual_seed(123)
LIST_LENGTH = 3
TENSOR_SIZE = torch.Size((3, 3))
TENSOR_SIZE_LIST = [TENSOR_SIZE for i in range(LIST_LENGTH)]
data = torch.rand(3, 3)
data_list = [torch.rand(3, 3) for i in range(LIST_LENGTH)]
grad = torch.rand(3, 3)
grad_list = [torch.rand(3, 3) for i in range(LIST_LENGTH)]
def check_send_recv_forward():
if gpc.get_local_rank(ParallelMode.PIPELINE) == 0:
device = torch.device('cuda:0')
data_to_send = data.to(device)
data_list_to_send = []
for data_in_list in data_list:
data_list_to_send.append(data_in_list.to(device))
send_forward(data_to_send)
send_forward(data_list_to_send)
else:
device = torch.device('cuda:1')
data_recv = recv_forward(TENSOR_SIZE)
data_list_recv = recv_forward(TENSOR_SIZE_LIST)
data_to_check = data.to(device)
assert data_recv.equal(data_to_check)
for data_recv, data_send in zip(data_list_recv, data_list):
data_to_check = data_send.to(device)
assert data_recv.equal(data_to_check)
def check_send_recv_backward():
if gpc.get_local_rank(ParallelMode.PIPELINE) == 0:
device = torch.device('cuda:0')
grad_recv = recv_backward(TENSOR_SIZE)
grad_list_recv = recv_backward(TENSOR_SIZE_LIST)
grad_to_check = grad.to(device)
assert grad_recv.equal(grad_to_check)
for grad_recv, grad_send in zip(grad_list_recv, grad_list):
grad_to_check = grad_send.to(device)
assert grad_recv.equal(grad_to_check)
else:
device = torch.device('cuda:1')
grad_to_send = grad.to(device)
grad_list_to_send = []
for grad_in_list in grad_list:
grad_list_to_send.append(grad_in_list.to(device))
send_backward(grad_to_send)
send_backward(grad_list_to_send)
def check_send_recv_forward_backward():
if gpc.get_local_rank(ParallelMode.PIPELINE) == 0:
device = torch.device('cuda:0')
data_list_to_send = []
for data_in_list in data_list:
data_list_to_send.append(data_in_list.to(device))
grad_list_recv = send_forward_recv_backward(data_list_to_send, TENSOR_SIZE_LIST)
for grad_recv, grad_send in zip(grad_list_recv, grad_list):
grad_to_check = grad_send.to(device)
assert grad_recv.equal(grad_to_check)
else:
device = torch.device('cuda:1')
grad_list_to_send = []
for grad_in_list in grad_list:
grad_list_to_send.append(grad_in_list.to(device))
data_list_recv = send_backward_recv_forward(grad_list_to_send, TENSOR_SIZE_LIST)
for data_recv, data_send in zip(data_list_recv, data_list):
data_to_check = data_send.to(device)
assert data_recv.equal(data_to_check)
def check_layer(rank, world_size, port):
launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
check_send_recv_forward()
check_send_recv_backward()
check_send_recv_forward_backward()
gpc.destroy()
torch.cuda.empty_cache()
@pytest.mark.dist
@rerun_if_address_is_in_use()
def test_object_list_p2p():
spawn(check_layer, 2)
if __name__ == '__main__':
test_object_list_p2p()